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  Violence and Statehood in India, 1950-56}
  % Authors
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  \vspace{.5em} Kaitlyn Webster\hspace{3em}
kaitlyn.webster@duke.edu\hspace{3em}
  Duke University
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  \headerbox{Summary}{name=abstract,column=0,row=0}{
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   {} Lacina (2014) examines India's 1956 federal reorganization, trying to understand \textbf{why some linguistic groups gained statehood} (or not), and \textbf{how violence affected} that outcome. I show that the \textbf{key variable can be omitted} from the model without worsening fit, and incorporating \textbf{spatial effects} improves predictive performance.
 }

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  \headerbox{Data and Research Design}{name=data,column=0,below=abstract}{
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    {} There are 63 different linguistic groups, resulting in 63 observations. Statehood, the dependent variable, is binary.  
  
\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
\includegraphics[width=1\linewidth]{IndiaplotA}
\end{tabular}

}

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  \headerbox{To Include or Not to Include?}{name=supp1,column=0,below=data, above = bottom}{
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{} Most of Lacina's \textbf{key variables do not noticeably improve model performance.} Separation plots (displayed in center panel) show predictive performance improves slightly after omitting her three variables of interest. Her results are driven primarily by the variable ``distance to capital." \\

\vspace{.5cm}
\centering
\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}

\begin{tabular}{llr}
	\hline 
	\multicolumn{2}{c}{Perf. Statistics} \\
	\cline{1-2}\\
	AIC    & LnLikelihood & Model \\
	\hline
	67.795   & -25    & Baseline      \\
	67.839      & -25.9   & No interact.   \\
	66.723      & -26.3     & No viol.      \\
	65.223  & -26.6     & No key vars      \\
	\hline
\end{tabular}
\end{tabular} 

}

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  \headerbox{Model Estimates}{name=coefplot,column=1,span=1,row=0}{
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\centering
\textbf{Baseline Model: Does a Linguistic Group Attain Statehood?}\\

\vspace{.5cm}
    \includegraphics[width=.994\linewidth, height = 4.1cm]{CoefPlotBaselinea}
{} Key variables are \textbf{violence}, a group's \textbf{relative representation in India's National Congress (Rel INC rep)}, and their \textbf{interaction}. Only the latter is significant at the .05 level.

  }

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  \headerbox{Cross-Validation of Estimates}{name=robustness,column=2,span=1,row=0,}{  
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\centering


\textbf{Coefficient Estimates, Colored by Fold}\\
\vspace{.5cm}
    \includegraphics[width=1\linewidth, height=5cm]{CoefPlotKfold2a} \\
 {} Cross-validation demonstrates coefficient estimates are mostly consistent across the six folds, but key variables rarely retain significance.
  }

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  \headerbox{Measures of Model Fit}{name=modelfit,column=1,span=2,below=coefplot,}{
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\begin{multicols}{2}
		Lacina's baseline model performs well, but...
		
		\includegraphics[width=1\linewidth]{SepPlotBaseline}
		
		her key variables are largely irrelevant.
		
		\includegraphics[width=1\linewidth]{SepPlotNoKeyVars}
		
\end{multicols}
		



  }


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  \headerbox{Spatial Analysis: Accounting for Neighborhood Effects}{name=results,column=1,span=2,below=modelfit,above=bottom}{
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I calculated and included a spatial variable for whether a group's neighbors attained statehood. The variable is positive and statistically significant at the .05 level, and model performance improves slightly when accounting for spatial effects. 

\begin{multicols}{2}

\includegraphics[width=1\linewidth, height = 5.5cm]{SpatialCoefs}


\includegraphics[width=1\linewidth, height = 6.1cm]{RocPlotCombined}
\end{multicols}

  }
  
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  \headerbox{Cross-Validation of Model Fit}{name=outofsample,column=3,span=1,row=0}{
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Here I use a 6-fold cross-validation to assess the model's predictive performance both in- and out-of-sample. The model's performance is clearly constrained by the small number of observations.
\begin{center}
\small{\textbf{Separation Plot: In Sample Performance}}
\includegraphics[width=1\linewidth, height=5.5cm]{SepPlotTrainGreyFinal}

\small{\textbf{Separation Plot: Out-of-Sample Performance}}
\includegraphics[width=1\linewidth, height=5.5cm]{SepPlotFoldsTestGrey}
\end{center}
  }%
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  \headerbox{Conclusions}{name=postcoldmaps,column=3,span=1,above=bottom,below=outofsample}{
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Extensions: coefficient cross-validation, in and out sample testing, assessing performance without main variables, and creating new model with spatial variable. \\

Key takeaways include:

\begin{itemize}
	\item Overall, Lacina's model performs well in-sample. A small dataset hampers out-of-sample performance.
	\item Key variables for Lacina's theory do not contribute substantially to the model. Model performance is improved slightly when they are removed.
	\item Spatial effects are subtle but do matter. This relationship would likely strengthen with better spatial data for the individual language groups.
\end{itemize}


  }%

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